Modular Software Suite

We are confident in the superiority of our software modules and therefore do not use tactics such as bundling. Rather, our software modules can be bought and used separately or in combinations.

Current modules that are fully built, tested, and vetted to be best in the market by customers include the following, and additional functionality can be customized.

Dynamic Allocation Optimizer. It optimally allocates inventory to campaigns so as to maximize the seller’s profit, subject to satisfying all constraints. The allocation is optimized down to the level of avails. It also includes re-optimization and automated optimized makegoods.

Dynamic Schedule Optimizer. This module takes the allocation optimization beyond avails down to individual spots. It also includes re-optimization and automated optimized makegoods. This module works holistically with the Dynamic Allocation Optimizer, so actually the allocation and schedule are optimized together in one optimization—which is necessary for true optimization. Our allocation and scheduling optimization is the only technology that can truly optimize these problems—as has been proven out several times.

Copy Assignment Optimizer. This module optimally allocates copy to the scheduled spots, subject to satisfying the copy constraints such as copy rotation. It also includes re-optimization and automated optimized makegoods. This module works holistically with the Dynamic Allocation Optimizer and the Dynamic Schedule Optimizer, so actually the allocation, schedule, and copy assignment are optimized together in one optimization—which is necessary for true optimization.

Multi-Proposal Optimizer & Sales Platform. This module is for optimal proposal generation. By proposal generation we mean optimizing which campaign requests to accept versus reject, and what inventory to use to satisfy them. Our systems are the only ones that can truly optimize this problem. The module also has UIs and patents-pending workflows.

This module works holistically with the Dynamic Allocation Optimizer, the Dynamic Schedule Optimizer, and the Copy Assignment Optimizer, so actually the allocation, schedule, copy assignment, and proposal generation (and proposal request acceptance/rejection) are optimized together in one optimization—which is necessary for true optimization. All of the modules can optimize audience-based, spot-based, and GRP-based campaigns soundly together.

They can be used for manual direct sales and for programmatic sales. They can dynamically optimize what inventory goes into what channel based on demand. So, there is no need to pre-allocate inventory across channels. Rather that decision can be truly optimized.

The module can optimize multiple campaign requests together, which leads to significantly better solutions than restricting attention to merely one campaign request at a time—as nascent other tools in the industry do.

Our technology has been designed from the ground up to be cross media, so it is not specific to linear TV, but can also handle nonlinear TV, Internet display advertising, etc.

This module also enables the user to control the functionality of the optimization in rich ways. It enables workflows that are automated as well as ones with manual approvals. It also supports over 50 different kinds of heatmap to visualize in rich ways how good different spots would be for reaching different audiences.

Impression Supply Prediction Engine. This module conducts impression-based audience prediction at the desired audience targeting granularity. As input, the module can use, for example, STB datasets, bought attribute data, and aggregations thereof. This module is highly customizable and can include unparalleled machine learning to handle seasonality and all other aspects of supply prediction.

We support seller-defined segments at any desired granularity. In addition, our proprietary technology enables buyer-customizable segments based on unlimited multi-attribute targeting, and provides high-quality impression estimates for both customer requests and internal requests (e.g., from the optimizer). As an example, say 50% of Californians are women, and 3% of people are surfers. If a customer targets Californian women who surf, 50% * 3% of overall Californian impressions may not be accurate anymore—the correct answer would be more in this case, and could be less in other cases. In other words, assuming independence between attributes is not accurate. Our technology is fully developed to support such cases. Using the input data, we generate a computationally-efficient representation of impression supply that optimally takes into account all such interdependencies simultaneously.

Sell-Side Pricing Optimization Engines. In our Sell-Side Pricing Optimization Engines, pricing can be based on historical campaign accept/reject data using world-leading novel machine learning, and it can be based on sophisticated optimization in light of the supply/demand-balance in all inventory segments and campaigns simultaneously. The pricing engines leverage our allocation optimization. These engines can output prices for both spot-based and audience-based campaigns. This can help in yield management beyond publisher’s current sell-side pricing approaches even when they are supplemented with our buy-side audience-targeted expressions of willingness to pay. The pricing engines can be customized to the publisher’s needs.

The other Optimized Markets products also work with sell-side prices (e.g., spot rate cards and audience segment CPM rates) from other sources, such as directly from the publisher. In that case, that pricing is used as the basis for our optimizations. Regardless of which sell-side pricing is used, our buy-side targeting and buy-side offer pricing (with reserve pricing, i.e., minimum pricing, from the sell side) can be used if desired.

Fill Prediction Engine. This module can continuously predict to what extent different inventory elements (e.g., different breaks in linear TV) will sell before airing. Optimized Markets has a sophisticated proprietary fill prediction algorithm that leverages our world-leading allocation optimization technology. In brief, the fill prediction algorithm considers current orders and possible streams of future orders (projected from training data) when making fill predictions.

In addition to these products, we offer optimization-based tools for “what-if” over-sell and under-sell analysis.